基于随机森林模型的中国中老年人群HDL-C环境影响因素研究

doi:10.12082/dqxxkx.2022.210823

地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (7): 1286-1300.doi: 10.12082/dqxxkx.2022.210823

• 地理空间分析综合应用 • 上一篇下一篇

基于随机森林模型的中国中老年人群HDL-C环境影响因素研究

裴泽华¹(), 葛淼¹^,^*(), 李浩¹, 何进伟², 王聪霞³

1.陕西师范大学地理科学与旅游学院健康地理研究所,西安 710119
2.延安大学医学院,延安 716000
3.西安交通大学医学院第二附属医院心内科,西安 710004

收稿日期:2021-12-22 修回日期:2022-03-22 出版日期:2022-07-25 发布日期:2022-09-25
通讯作者: * 葛淼（1960—）,男,陕西咸阳人,教授,主要从事健康地理研究。E-mail: gemiao@snnu.edu.cn
作者简介:裴泽华（1997—）,男,山西长治人,硕士生,主要从事健康地理研究。E-mail: pzh15635445598@163.com
基金资助:
国家自然科学基金项目(41761100)

Environmental Factors Influencing HDL-C in Middle-aged and Elderly Chinese Population based on Random Forest Model

PEI Zehua¹(), GE Miao¹^,^*(), LI Hao¹, HE Jinwei², WANG Congxia³

1. Institute of Health Geography, School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
2. Medical College of Yan'an University,Yan'an 716000, China 3. The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, China
3. The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, China

Received:2021-12-22 Revised:2022-03-22 Online:2022-07-25 Published:2022-09-25
Contact: GE Miao
Supported by:
National Natural Science Foundation of China(41761100)

摘要/Abstract

摘要：

高密度脂蛋白胆固醇（HDL-C）可以有效促进人体内胆固醇的代谢外排,其水平的高低与患心血管疾病的风险呈负相关关系,是心血管疾病的预防与保护因素。厘清我国中老年人群HDL-C水平的地理分异特征及环境影响因素,对我国心血管疾病防治工作的开展有重要意义。论文基于中国中老年人纵向追踪调查,利用全局空间自相关、冷热点分析等方法阐释中国中老年人群HDL-C水平的空间分异特征及变化趋势,同时对比引入随机森林回归模型及多元线性回归方法探讨HDL-C水平空间分布的环境影响因素及其指示作用。结果表明：中国中老年人群HDL-C水平表现为女性高于男性、农村高于城镇,具有明显的地域差异性,整体呈现出“北低南高,中间过渡”的分布格局,且北方出现了以内蒙古、河北、辽宁为代表的低值聚集区,南方出现了以广东、广西、云南为代表的高值聚集区;SO₂、NO₂、降水、气压、PM₁₀和PM_2.5是影响中老年人群HDL-C水平差异分布的主要环境因素,其中高浓度的空气污染物是造成HDL-C值较低的危险因素,充沛的降水和低压环境是防治HDL-C值较低的保护因素。因此,今后关于HDL-C血脂异常防控工作在全国各地应注重其空间分布规律,重点加强对HDL-C低值区的监测,以达到因地制宜、精准防控的目的。

关键词: 中老年人群, 高密度脂蛋白胆固醇, 时空变化特征, 气象条件, 空气污染, 空间相关分析, 随机森林, 影响因素

Abstract:

High Density Lipoprotein Cholesterol (HDL-C) can effectively promote the metabolic efflux of cholesterol in human body, and its level is negatively correlated with the risk of cardiovascular disease, which is a preventive and protective factor of cardiovascular disease. It is of great significance to clarify the geographical characteristics and environmental factors of HDL-C level in middle-aged and elderly population in China. Based on the longitudinal survey of middle-aged and elderly people in China, this paper uses global spatial autocorrelation and cold hot spot analysis to explain the spatial characteristics and trends of HDL-C levels in middle-aged and elderly people in China. At the same time, the random forest regression model and multiple linear regression method are compared to explore the environmental factors influencing the spatial distribution of HDL-C level. The results show that the HDL-C level of middle-aged and elderly population in China is higher in females than that in males, and higher in rural areas than that in urban areas, with obvious regional differences. The overall distribution pattern is "low in the north and high in the south, with transition in the middle". In addition, there are low value aggregation areas in Inner Mongolia, Hebei, and Liaoning in the north and high value aggregation areas in Guangdong, Guangxi, and Yunnan in the south. The SO₂, NO₂, precipitation, air pressure, PM₁₀ and PM_2.5 are the main environmental factors affecting the different distributions of HDL-C level in middle-aged and elderly population. Among them, high concentration of air pollutants is the risk factor of low HDL-C value, while abundant precipitation and low-pressure environment are the protective factors to prevent and control low HDL-C value. Therefore, the prevention and control of HDL-C dyslipidemia should pay attention to its spatial distribution throughout the country in the future, focusing on strengthening the monitoring of HDL-C low value areas, so as to achieve the purpose of adjusting measures to local conditions and accurate prevention and control.

Key words: middle-aged and old people, high-density lipoprotein cholesterol, spatial-temporal variation characteristics, meteorological conditions, air pollution, spatial correlation analysis, random forest, influence factor

裴泽华, 葛淼, 李浩, 何进伟, 王聪霞. 基于随机森林模型的中国中老年人群HDL-C环境影响因素研究[J]. 地球信息科学学报, 2022, 24(7): 1286-1300.DOI:10.12082/dqxxkx.2022.210823

PEI Zehua, GE Miao, LI Hao, HE Jinwei, WANG Congxia. Environmental Factors Influencing HDL-C in Middle-aged and Elderly Chinese Population based on Random Forest Model[J]. Journal of Geo-information Science, 2022, 24(7): 1286-1300.DOI:10.12082/dqxxkx.2022.210823

图/表 11

图1

表1

普通克里金插值半变异函数及交叉验证精度指标

影响因素	半变异函数	平均绝对误差 $d -$	均方根误差RMSE	平均相对误差 $d -$ %
气压	高斯模型	24.024	35.892	2.869
气温	高斯模型	1.339	2.196	28.889
湿度	高斯模型	3.746	5.012	6.546
降水	高斯模型	134.555	196.567	24.883
NO₂	指数模型	6.015	7.794	24.571
SO₂	指数模型	5.445	7.866	26.013
CO	指数模型	0.167	0.228	17.251
O₃	指数模型	10.399	13.838	11.603
PM_2.5	指数模型	4.644	6.367	10.205
PM₁₀	指数模型	8.331	11.751	10.908

表1

表2

表3

图2

图3

表4

图4

图5

图6

图7

参考文献 53

[1]	Li M, Wang Z Q, Zhang L, et al. Burden of cirrhosis and other chronic liver diseases caused by specific etiologies in China, 1990-2016: Findings from the global burden of disease study 2016[J]. Biomedical and Environmental Sciences, 2020, 33(1):1-10. DOI: 10.3967/bes2020.001 doi: 10.3967/bes2020.001
[2]	Zhou M G, Wang H D, Zeng X Y. Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: A systematic analysis for the global burden of disease study 2017[J]. Lancet, 2020, 396(10243):26. DOI: 10.1016/S0140-6736(19)30427-1 doi: 10.1016/S0140-6736(19)30427-1
[3]	Li X, Wu C, Lu J. Cardiovascular risk factors in China: A nationwide population-based cohort study[J]. Lancet Public Health, 2021, 6(5):E271.DOI: 10.1016/S2468-2667(20)30191-2 doi: 10.1016/S2468-2667(20)30191-2
[4]	Wang L, Xiang F F, Ji J, et al. Indoxyl sulfate and high-density lipoprotein cholesterol in early stages of chronic kidney disease[J]. Renal Failure, 2020, 42(1):1157-1163. DOI: 10.1080/0886022X.2020.1845731 doi: 10.1080/0886022X.2020.1845731
[5]	鲍俊杰, 李铁铮, 杨宗明, 等. 血清高密度脂蛋白胆固醇水平与心脑血管疾病死亡的相关性[J]. 中华预防医学杂志, 2021, 55(12):1461-1467. pmid: 34963244
	[ Bao J J, Li T Z, Yang Z M, et al. Relationship between serum high density lipoprotein cholesterol level and cardiovascular disease death[J]. Chinese Journal of Preventive Medicine, 2021, 55(12):1461-1467. ] DOI: 10.3760/cma.j.cn112150-20201217-01458 doi: 10.3760/cma.j.cn112150-20201217-01458 pmid: 34963244
[6]	Ma X C, Yan H C, Zhang H Z, et al. Progress in the seasonal variations of blood lipids: A mini-review[J]. Lipids in Health and Disease, 2020, 19(1). DOI: 10.1186/s12944-020-01237-3 doi: 10.1186/s12944-020-01237-3
[7]	Li A, Pei L, Zhao M D, et al. Investigating potential associations between O₃ exposure and lipid profiles: A longitudinal study of older adults in Beijing[J]. Environment International, 2019,133(A):105135. DOI: 10.1016/j.envint.2019.105135 doi: 10.1016/j.envint.2019.105135
[8]	周小明. 脂质代谢相关内外因素的临床和基础研究—气候和TSH[D]. 济南: 山东大学, 2016.
	[ Zhou X M. Clinical and basic research on internal and external factors related to lipid metabolism -- climate and TSH[D]. Jinan: Shandong University, 2016. ]
[9]	Baibas N, Trichopoulou A, Voridis E, et al. Residence in mountainous compared with lowland areas in relation to total and coronary mortality. A study in rural Greece[J]. Journal of Epidemiology and Community Health, 2005, 59(4):274-278. DOI: 10.1136/jech.2004.025510 doi: 10.1136/jech.2004.025510 pmid: 15767379
[10]	Juna C F, Cho Y H, Ham D, et al. Associations of relative humidity and lifestyles with metabolic syndrome among the Ecuadorian adult population: Ecuador national health and nutrition survey (ENSANUT-ECU) 2012[J]. International Journal of Environmental Research and Public Health, 2020, 17(23):9023. DOI: 10.3390/ijerph17239023. doi: 10.3390/ijerph17239023
[11]	肖三华. 环境空气污染短期和长期暴露的血脂效应研究[D]. 武汉: 华中科技大学, 2018.
	[ Xiao S H. Study on lipid effect of short-term and long-term exposure to ambient air pollution[D]. Wuhan: Huazhong University of Science and Technology, 2018. ]
[12]	Bell G, Mora S, Greenland P, et al. Association of air pollution exposures with high-density lipoprotein cholesterol and particle number[J]. Arteriosclerosis, Thrombosis, and Vascular Biology, 2017, 37(5):976-982. DOI: 10.1161/atvbaha.116.308193 doi: 10.1161/atvbaha.116.308193
[13]	Gaio V, Roquette R, Dias C M, et al. Ambient air pollution and lipid profile: Systematic review and meta-analysis[J]. Environmental Pollution, 2019, 254:113036. DOI: 10.1016/j.envpol.2019.113036 doi: 10.1016/j.envpol.2019.113036
[14]	Zhou X M, Lin H Y, Zhang S G, et al. Effects of climatic factors on plasma lipid levels: A 5-year longitudinal study in a large Chinese population[J]. Journal of Clinical Lipidology, 2016, 10(5):1119-1128. DOI: 10.1016/j.jacl.2016.06.009 doi: 10.1016/j.jacl.2016.06.009
[15]	Cai Y T, Hansell A L, Blangiardo M, et al. Long-term exposure to road traffic noise, ambient air pollution, and cardiovascular risk factors in the HUNT and lifelines cohorts[J]. European Heart Journal, 2017, 38(29):2290-2296. DOI: 10.1093/eurheartj/ehx263 doi: 10.1093/eurheartj/ehx263
[16]	Yang B Y, Qian Z M, Vaughn M G, et al. Overweight modifies the association between long-term ambient air pollution and prehypertension in Chinese adults: The 33 Communities Chinese Health Study[J]. Environmental Health: a Global Access Science Source, 2018, 17(1):57. DOI: 10.1186/s12940-018-0401-2 doi: 10.1186/s12940-018-0401-2
[17]	Zhang Q L, Liu C, Wang Y F, et al. Associations of long-term exposure to ambient nitrogen dioxide with indicators of diabetes and dyslipidemia in China: A nationwide analysis[J]. Chemosphere, 2021, 269:128724. DOI: 10.1016/j.chemosphere.2020.128724 doi: 10.1016/j.chemosphere.2020.128724
[18]	石琴, 郑山, 朱文芝, 等. NO₂对高血压患者血压和脉压的短期影响[J]. 中国环境科学, 2020, 40(8):3627-3635.
	[ Shi Q, Zheng S, Zhu W Z, et al. The short-term effects of NO₂ on blood pressure and pulse pressure in patients with hypertension[J]. China Environmental Science, 2020, 40(8):3627-3635. ] DOI: 10.19674/j.cnki.issn1000-6923.2020.0405 doi: 10.19674/j.cnki.issn1000-6923.2020.0405
[19]	代甜, 王敏珍, 靳亚飞, 等. 大气SO₂和NO₂对2型糖尿病发病影响的队列研究[J]. 兰州大学学报(医学版), 2021, 47(3):51-57.
	[ Dai T, Wang M Z, Jin Y F, et al. Long-term exposure to SO₂, NO₂ and incidence of type 2 diabetes mellitus: A cohort study[J]. Journal of Lanzhou University (Medical Sciences), 2021, 47(3):51-57. ]
[20]	施迅, 王法辉. 地理信息技术在公共卫生与健康领域的应用[M]. 北京: 高等教育出版社, 2016.
	[ Shi X, Wang F H. Applications of geospatial information technologies in public health[M]. Beijing: Higher Education Press, 2016. ]
[21]	杨振, 丁启燕, 周晴雨, 等. 长江中下游地区人口健康水平空间分异特征与地理影响因子[J]. 地理与地理信息科学, 2018, 34(6):77-84,2.
	[ Yang Z, Ding Q Y, Zhou Q Y, et al. Spatial differentiation and geographical factors of population health in the middle and lower reaches of the Yangtze River[J]. Geography and geo-information science, 2018, 34(6):77-84,2. ] DOI: 10.3969/j.issn.1672-0504.2018.06.012 doi: 10.3969/j.issn.1672-0504.2018.06.012
[22]	丘文洋, 李连发, 张杰昊, 等. 利用空间聚集的贝叶斯网络评估手足口病发病风险[J]. 地球信息科学学报, 2017, 19(8):1036-1048. doi: 10.3724/SP.J.1047.2017.01036
	[ Qiu W Y, Li L F, Zhang J H, et al. A Bayesian network method considering spatial cluster to evaluate health risk of hand, foot and mouth disease[J]. Journal of Geo-Information Science, 2017, 19(8):1036-1048. ] DOI: 10.3724/SP.J.1047.2017.01036 doi: 10.3724/SP.J.1047.2017.01036
[23]	葛淼, 薛然尹, 何进伟, 等. 中国男性儿童呼气高峰流量参考值地理分布[J]. 地理研究, 2014, 33(3):451-466. doi: 10.11821/dlyj201403005
	[ Ge M, Xue R Y, He J W, et al. Geographic distribution of reference value of boys' peak expiratory flow rate based on the artificial neural networks[J]. Geographical Research, 2014, 33(3):451-466. ] DOI: 10.11821/dlyj201403005. doi: 10.11821/dlyj201403005
[24]	张雷, 王琳琳, 张旭东, 等. 随机森林算法基本思想及其在生态学中的应用——以云南松分布模拟为例[J]. 生态学报, 2014, 34(3):650-659.
	[ Zhang L, Wang L L, Zhang X D, et al. The basic principle of random forest and its applications in ecology: A case study of Pinus yunnanensis[J]. Chinese Journal of Plant Ecology, 2014, 34(3):650-659. ] DOI: 10.5846/stxb201306031292. doi: 10.5846/stxb201306031292
[25]	夏晓圣, 陈菁菁, 王佳佳, 等. 基于随机森林模型的中国PM_2.5浓度影响因素分析[J]. 环境科学, 2020, 41(5):2057-2065.
	[ Xia X S, Chen J J, Wang J J, et al. PM_2.5 concentration influencing factors in China based on the random forest model[J]. Environmental Science, 2020, 41(5):2057-2065. ] DOI: 10.13227/j.hjkx.201910126 doi: 10.13227/j.hjkx.201910126
[26]	王超, 阚瑷珂, 曾业隆, 等. 基于随机森林模型的西藏人口分布格局及影响因素[J]. 地理学报, 2019, 74(4):664-680. doi: 10.11821/dlxb201904004
	[ Wang C, Kan A K, Zeng Y L, et al. Population distribution pattern and influencing factors in Tibet based on random forest model[J]. Acta Geographica Sinica, 2019, 74(4):664-680. ] DOI: 10.11821/dlxb201904004. doi: 10.11821/dlxb201904004.
[27]	闫广华, 陈曦, 张云. 基于随机森林模型的东北地区收缩城市分布格局及影响因素研究[J]. 地理科学, 2021, 41(5):880-889. doi: 10.13249/j.cnki.sgs.2021.05.016
	[ Yan G H, Chen X, Zhang Y. Shrinking Cities distribution pattern and influencing factors in northeast China based on random forest model[J]. Scientia Geographica Sinica, 2021, 41(5):880-889. ] DOI: 10.13249/j.cnki.sgs.2021.05.016 doi: 10.13249/j.cnki.sgs.2021.05.016
[28]	张坚, 满青青, 王春荣, 等. 中国18岁及以上人群血脂水平及分布特征[J]. 中华预防医学杂志, 2005, 5:8-11.
	[ Man Q Q, Wang C R, et al. Blood lipid levels and distribution in Chinese aged 18 years and older[J]. Chinese Journal of Preventive Medicine, 2005, 5:8-11. ] DOI: 10.3760/j:issn:0253-9624.2005.05.003 doi: 10.3760/j:issn:0253-9624.2005.05.003
[29]	李浩, 张明鑫, 汪冉. 区域地理环境因素对宁夏泾源县儿童呼吸系统疾病的影响[J]. 地理研究, 2019, 38(12):2889-2898. doi: 10.11821/dlyj020181236
	[ Li H, Zhang M X, Wang R. The effects of regional geographical factors on children's respiratory diseases in Jingyuan, Ningxia[J]. Geographical Research, 2019, 38(12):2889-2898. ] DOI: 10.11821/dlyj020181236. doi: 10.11821/dlyj020181236.
[30]	邢晓语, 杨秀春, 徐斌, 等. 基于随机森林算法的草原地上生物量遥感估算方法研究[J]. 地球信息科学学报, 2021, 23(7):1312-1324. doi: 10.12082/dqxxkx.2021.200605
	[ Xing X Y, Yang X C, Xu B, et al. Remote sensing estimation of grassland aboveground biomass based on random forest[J]. Journal of Geo-information Science, 2021, 23(7):1312-1324. ] DOI: 10.12082/dqxxkx.2021.200605 doi: 10.12082/dqxxkx.2021.200605
[31]	Guidotti R, Monreale A, Ruggieri S, et al. A survey of methods for explaining black box models[J]. ACM Computing Surveys, 2019, 51(5):93. DOI: 10.1145/3236009 doi: 10.1145/3236009
[32]	Friedman J H. Greedy function approximation: A gradient boosting machine[J]. Annals of Statistics, 2001, 29(5):1189-1232. DOI: 10.1214/aos/1013203451 doi: 10.1214/aos/1013203451
[33]	Dowling E A. How exercise affects lipid profiles in women: What to recommend for patients[J]. The Physician and Sportsmedicine, 2001, 29(9):45-52. DOI: 10.3810/psm.2001.09.972 doi: 10.3810/psm.2001.09.972
[34]	王晨冉, 孟显峰, 王春平, 等. 1990-2017年中国人群缺血性心脏病疾病负担及其危险因素变化趋势研究[J]. 中华流行病学杂志, 2020, 41(10):1703-1709. pmid: 33297630
	[ Wang C R, Meng X F, Wang C P. et al. Trends of disease burden and risk factors of ischemic heart disease in Chinese population from 1990 to 2017[J]. Chinese journal of epidemiology, 2020, 41(10):1703-1709. ] DOI: 10.3760/cma.j.cn1123 38-20191018-00743 doi: 10.3760/cma.j.cn112338-20191018-00743 pmid: 33297630
[35]	Xiao S H, Liu R R, Wei Y X, et al. Air pollution and blood lipid markers levels: Estimating short and long-term effects on elderly hypertension inpatients complicated with or without type 2 diabetes[J]. Environmental Pollution (Barking, Essex: 1987), 2016, 215:135-140. DOI: 10.1016/j.envpol.2016.05.007 doi: 10.1016/j.envpol.2016.05.007
[36]	Wang M Z, Zheng S, Nie Y H, et al. Association between short-term exposure to air pollution and dyslipidemias among type 2 diabetic patients in northwest China: A population-based study[J]. International Journal of Environmental Research and Public Health, 2018, 15(4):631. DOI: 10.3390/ijerph15040631 doi: 10.3390/ijerph15040631
[37]	Mao S Y, Chen G B, Liu F F, et al. Long-term effects of ambient air pollutants to blood lipids and dyslipidemias in a Chinese rural population[J]. Environmental Pollution, 2020, 256:113403. DOI: 10.1016/j.envpol.2019.113403 doi: 10.1016/j.envpol.2019.113403
[38]	Li J P, Zhou C P, Xu H B, et al. Ambient air pollution is associated with HDL (high-density lipoprotein) dysfunction in healthy adults[J]. Arteriosclerosis, Thrombosis, and Vascular Biology, 2019, 39(3):513-522. DOI: 10.1161/ATVBAHA.118.311749 doi: 10.1161/ATVBAHA.118.311749
[39]	Rajagopalan S, Brook R D. Air pollution and type 2 diabetes: Mechanistic insights[J]. Diabetes, 2012, 61(12):3037-3045. DOI: 10.2337/db12-0190 doi: 10.2337/db12-0190 pmid: 23172950
[40]	Lovati M R, Manzoni C, Daldossi M, et al. Effects of sub-chronic exposure to SO₂ on lipid and carbohydrate metabolism in rats[J]. Archives of Toxicology, 1996, 70(3/4):164-173. DOI: 10.1007/s002040050256 doi: 10.1007/s002040050256
[41]	庞慧芳, 陈伯望, 吴超群, 等. DNA甲基化在长期空气污染暴露与心血管疾病高危人群血脂水平关系中的中介效应[J]. 环境与职业医学, 2021, 38(1):10-16.
	[ Chen B W, Wu C Q, et al. Mediation effect of DNA methylation on relationship between long-term air pollution exposure and blood lipid level in population at high risk for cardiovascular disease[J]. Journal of Environmental and Occupational Medicine, 2021, 38(1):10-16. ] DOI: 10.13213/j.cnki.jeom.2021.20397 doi: 10.13213/j.cnki.jeom.2021.20397
[42]	Wu X M, Basu R P, Malig B, et al. Association between gaseous air pollutants and inflammatory, hemostatic and lipid markers in a cohort of midlife women[J]. Environment International, 2017, 107:131-139. DOI: 10.1016/j.envint.2017.07.004 doi: 10.1016/j.envint.2017.07.004
[43]	Zhang K H, Wang H Y, He W L, et al. The association between ambient air pollution and blood lipids: A longitudinal study in Shijiazhuang, China[J]. The Science of the Total Environment, 2021, 752:141648. DOI: 10.1016/j.scitotenv.2020.141648 doi: 10.1016/j.scitotenv.2020.141648
[44]	Paoin K, Ueda K, Vathesatogkit P, et al. Long-term air pollution exposure and serum lipids and blood sugar: A longitudinal cohort study from the electricity generating authority of Thailand study[J]. Atmospheric Environment, 2021, 259:118515. DOI: 10.1016/j.atmosenv.2021.118515 doi: 10.1016/j.atmosenv.2021.118515
[45]	Brucker N, Moro A M, Charão M F, et al. Biomarkers of occupational exposure to air pollution, inflammation and oxidative damage in taxi drivers[J]. The Science of the Total Environment, 2013, 463/464:884-893. DOI: 10.1016/j.scitotenv.2013.06.098 doi: 10.1016/j.scitotenv.2013.06.098
[46]	Juna C F, Cho Y, Ham D, et al. Association of carbohydrate and fat intake with prevalence of metabolic syndrome can be modified by physical activity and physical environment in Ecuadorian adults: The ENSANUT-ECU study[J]. Nutrients, 2021, 13(6):1834. DOI: 10.3390/nu13061834 doi: 10.3390/nu13061834
[47]	Leitte A M, Petrescu C, Franck U, et al. Respiratory health, effects of ambient air pollution and its modification by air humidity in Drobeta-Turnu Severin, Romania[J]. The Science of the Total Environment, 2009, 407(13):4004-4011. DOI: 10.1016/j.scitotenv.2009.02.042 doi: 10.1016/j.scitotenv.2009.02.042
[48]	Domínguez Coello S, et al. Cabrera De León A, Bosa Ojeda F,High density lipoprotein cholesterol increases with living altitude[J]. International Journal of Epidemiology, 2000, 29(1):65-70. DOI: 10.1093/ije/29.1.65 doi: 10.1093/ije/29.1.65 pmid: 10750605
[49]	Al Riyami N B, Banerjee Y, Al-Waili K, et al. The effect of residing altitude on levels of high-density lipoprotein cholesterol: A pilot study from the Omani Arab population[J]. Angiology, 2015, 66(6):568-573. DOI: 10.1177/0003 319714544355 doi: 10.1177/0003 319714544355
[50]	Li J, Yao Y, Xie W X, et al. Association of long-term exposure to PM _2.5 with blood lipids in the Chinese population: Findings from a longitudinal quasi-experiment[J]. Environment International, 2021, 151:106454. DOI: 10.1016/j.envint.2021.106454 doi: 10.1016/j.envint.2021.106454
[51]	Yitshak Sade M, Kloog I, Liberty I F, et al. The association between air pollution exposure and glucose and lipids levels[J]. The Journal of Clinical Endocrinology and Metabolism, 2016, 101(6):2460-2467. DOI: 10.1210/jc.2016-1378 doi: 10.1210/jc.2016-1378 pmid: 27218271
[52]	郑茹敏, 梅林, 姜洪强, 等. 基于随机森林模型的中国流动人口社会融合空间差异及影响因素[J]. 地理科学, 2021, 41(10):1763-1772. doi: 10.13249/j.cnki.sgs.2021.10.008
	[ Mei L, Jiang H Q, et al. Spatial differences and impact factors of migrant integration in China based on random forest model[J]. Scientia Geographica Sinica, 2021, 41(10):1763-1772. ] DOI: 10.13249/j.cnki.sgs.2021.10.008 doi: 10.13249/j.cnki.sgs.2021.10.008
[53]	赵艳艳, 张晓平, 陈明星, 等. 中国城市空气质量的区域差异及归因分析[J]. 地理学报, 2021, 76(11):2814-2829. doi: 10.11821/dlxb202111015
	[ Zhao Y Y, Zhang X P, Chen M X, et al. Regional variation of urban air quality in China and its dominant factors[J]. Acta Geographica Sinica, 2021, 76(11):2814-2829. ] DOI: 10.11821/dlxb202111015 doi: 10.11821/dlxb202111015

年份	人数/人	城乡类型	性别/（mg/dl）	差值/(mg/dl)	合计/(mg/dl)
2011年	11 663	合计	男：50.34±16.25	0.94	50.84±15.32
		合计	女：51.28±14.47	0.94	50.84±15.32
		城镇	男：47.52±15.58	1.93	48.58±14.72
		城镇	女：49.45±13.91	1.93	48.58±14.72
		农村	男：51.92±16.40	0.46	52.16±15.52
		农村	女：52.38±14.69	0.46	52.16±15.52
2015年	13 354	合计	男：49.97±12.32	2.22	51.17±11.52
		合计	女：52.19±10.70	2.22	51.17±11.52
		城镇	男：48.20±11.18	3.04	49.87±10.77
		城镇	女：51.24±10.22	3.04	49.87±10.77
		农村	男：51.01±12.83	1.79	51.97±11.90
		农村	女：52.80±10.94	1.79	51.97±11.90

	Moran′s I 指数		Z值		P值		类型
	2011年	2015年	2011年	2015年	2011年	2015年	2011年	2015年
城镇	0.011	0.112	1.351	8.087	0.177	0.000	Random	Clustered
农村	0.124	0.123	8.815	8.835	0.000	0.000	Clustered	Clustered

影响因素	序号	相关系数r	VIF
气压	A1	-0.167	2.331
气温	A2	0.293**	5.482
湿度	A3	0.333**	8.681
降水	A4	0.327**	6.746
SO₂	B1	-0.441**	3.516
NO₂	B2	-0.359**	4.349
CO	B3	-0.197*	3.972
O₃	B4	-0.216*	2.447
PM_2.5	B5	-0.337**	19.120
PM₁₀	B6	-0.373**	14.155

基于随机森林模型的中国中老年人群HDL-C环境影响因素研究

Environmental Factors Influencing HDL-C in Middle-aged and Elderly Chinese Population based on Random Forest Model

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